title: Beyond long memory in heart rate variability: an approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity.
abstract: Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.
authors.affiliation: Departamento de Matemática, Escola de Cie^ncias e Tecnologia, Universidade de Trás-os-Montes e Alto Douro and CM-UTAD, Portugal.
authors.forename: Argentina
authors.initials: A
authors.lastname: Leite
authors.forename: Ana Paula
authors.initials: AP
authors.lastname: Rocha
authors.forename: Maria Eduarda
authors.initials: ME
authors.lastname: Silva
mesh_terms: D000328:Adult; D001281:Atrial Fibrillation / Q000503:physiopathology; D004562:Electrocardiography; D006333:Heart Failure / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D012044:Regression Analysis; D013223:Statistics as Topic; D013997:Time Factors